1724 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 20, NO. 11, NOVEMBER 2009
Real-Time Robot Path Planning Based on a Modified
Pulse-Coupled Neural Network Model
Hong Qu, Simon X. Yang, Senior Member, IEEE, Allan R. Willms, and Zhang Yi
Abstract—This paper presents a modified pulse-coupled
neural network (MPCNN) model for real-time collision-free path
planning of mobile robots in nonstationary environments. The
proposed neural network for robots is topologically organized
with only local lateral connections among neurons. It works in
dynamic environments and requires no prior knowledge of target
or barrier movements. The target neuron fires first, and then the
firing event spreads out, through the lateral connections among
the neurons, like the propagation of a wave. Obstacles have no
connections to their neighbors. Each neuron records its parent,
that is, the neighbor that caused it to fire. The real-time optimal
path is then the sequence of parents from the robot to the target.
In a static case where the barriers and targets are stationary, this
paper proves that the generated wave in the network spreads out-
ward with travel times proportional to the linking strength among
neurons. Thus, the generated path is always the global shortest
path from the robot to the target. In addition, each neuron in the
proposed model can propagate a firing event to its neighboring
neuron without any comparing computations. The proposed
model is applied to generate collision-free paths for a mobile robot
to solve a maze-type problem, to circumvent concave U-shaped
obstacles, and to track a moving target in an environment with
varying obstacles. The effectiveness and efficiency of the proposed
approach is demonstrated through simulation and comparison
studies.
Index Terms—Collision avoidance, mobile robot, neural dy-
namics, path planning, pulse-coupled neural networks (PCNNs),
spiking, wave.
I. INTRODUCTION
R
EAL-TIME planning of a collision-free path for a mobile
robot to move from an initial position to a target position
in a nonstationary environment is a well-known problem in
robotics. Since the earliest work by Lozano-Perez and Wesley
Manuscript received December 01, 2007; revised April 23, 2009 and July
27, 2009; accepted July 30, 2009. First published September 22, 2009; current
version published November 04, 2009. This work was supported by the National
Science and Engineering Research Council (NSERC) of Canada, the National
Science Foundation of China under Grants 60905037 and 60802064, and the
Specialized Research Fund for the Doctoral Program of Higher Education of
China under Grant 200806141049.
H. Qu is with the School of Computer Science and Engineering, University
of Electronic Science and Technology of China, Chengdu 610054, China and
also with the School of Engineering, University of Guelph, Guelph, ON N1G
2W1 Canada (e-mail: HongQu@uestc.edu.cn).
S. X. Yang is with the Advanced Robotics and Intelligent System (ARIS)
Laboratory, School of Engineering, University of Guelph, Guelph, ON N1G
2W1 Canada (e-mail: syang@uoguelph.ca).
A. R. Willms is with the Department of Mathematics and Statistics, Univer-
sity of Guelph, Guelph, ON N1G 2W1 Canada (e-mail: awillms@uoguelph.ca).
Z. Yi is with the School of Computer Science, Sichuan University, Chengdu
610054, China.
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TNN.2009.2029858
in 1979 [1], which presented an algorithm to avoid polyhedral
obstacles based on a visibility graph, various methods have
been proposed to solve the robot path-planning problem. Global
methods such as road map [2], cell decomposition [3]–[6], and
distance transform [7] were able to search possible paths in the
whole workspace, although these methods are mainly suitable
to static environments, and are computationally expensive
in complex environments. Many heuristic approaches, such
as potential field methods [8]–[13] and the grid-based
algorithm [14]–[16], have been proposed to provide effective
path searching. Some of those methods suffer from undesired
local minima, i.e., the robot may be trapped in some cases
such as with concave U-shaped barriers. In general, the global
approaches would suffer from slow computation for implemen-
tation in complex environments, while the local planners have
difficulty to find a reasonable solution to some situations with
local minima.
Some new approaches have also been reported in recent years.
Li and Bui [17] proposed a fluid model for robot path plan-
ning in a static environment. Ong and Gilbert [18] proposed a
new model for path planning with penetration growth distance,
which can generate optimal continuous robot paths in static en-
vironments. Oriolo
et al. [19] proposed a model for real-time
map building and navigation for a mobile robot, where a global
path-planning algorithm, a local graph search algorithm, and
several cost functions are used. Hu and Yang [20] proposed an
efficient knowledge-based genetic algorithm for real-time path
planning of mobile robots, where five genetic operators were de-
signed particularly for robot path planning. Lebedev et al. [21]
presented a dynamic wave expansion model for robot motion
planning in time-varying environments, which is claimed to be
parameter free, and computationally efficient in dynamic envi-
ronments. More recently, Willms and Yang [22], [23] proposed
an efficient dynamic programming algorithm for real-time robot
path planning, which is reported to be suitable to situations
where targets and barriers are permitted to move.
Since the original work of Hopfield [24] in 1985, much
research has been done using neural networks to solve combi-
natorial optimization problems [25]–[27]. Some neural network
models have been proposed to generate real-time robot trajec-
tories. Zalama et al. [28] proposed a neural network model
for mobile robot navigation. Kohonen’s self-organizing map
(SOM)-based model that learns the transformation from the
Cartesian workspace to the joint space of robot manipulators
was studied by Ritter et al. [29]. Zhu and Yang [30] proposed
a SOM-based model for dynamic task assignment and path
planning of multirobots. Glasius et al. [31]–[33] proposed a
Hopfield-type neural network for real-time trajectory genera-
tion with obstacle avoidance in a nonstationary environment.
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